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    This study introduces a deep neural network (DNN) for faster wavefront correction in adaptive optics (AO). The DNN method significantly reduces latency in free-space optics communication by directly detecting aberrations from images.

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    Area of Science:

    • Optical Engineering
    • Computational Optics
    • Adaptive Optics

    Background:

    • Wavefront sensorless (WFS-less) adaptive optics (AO) systems face latency issues due to iterative search algorithms.
    • This latency is particularly problematic for free-space optics communication applications.

    Purpose of the Study:

    • To develop a novel deep neural network (DNN)-based method for aberration correction in AO systems.
    • To overcome the latency limitations of existing WFS-less AO techniques.

    Main Methods:

    • A deep neural network (DNN) model was designed to directly detect wavefront distortions from intensity images.
    • The method incorporates Zernike coefficients for tip-and-tilt modes, potentially eliminating the need for a separate tip-tilt correction system.

    Main Results:

    • The DNN-based method effectively reduces computation time compared to traditional iterative approaches.
    • Significant improvements in root mean square (RMS) aberration correction were observed across various turbulence conditions.

    Conclusions:

    • The proposed DNN method offers a promising solution for high-speed aberration correction in AO systems.
    • This approach has the potential to enhance the performance and reliability of free-space optics communication.